Network equipment operation adjustment system

ABSTRACT

A network equipment operation adjustment system is provided herein that is configured to improve the performance of a telecommunications network by generating a network score representing the performance of a telecommunications network within a geographic region, determining one or more network equipment parameter adjustments using the network score, and causing the adjustments to occur. The network equipment operation adjustment system can further display the network score and other network scores for other geographic regions in an interactive user interface to efficiently allow a network operator to view the network performance of a telecommunications network by geographic region and/or to view how the network performance in each of the geographic regions is changing over time.

BACKGROUND

A core network (also known as network core or backbone network) is thecentral part of a telecommunications network that provides variousservices to telecommunication devices, often referred to as userequipment (“UE”), that are connected by access network(s) of thetelecommunications network. Typically, a core network includes highcapacity communication facilities that connect primary nodes, andprovides paths for the exchange of information between differentsub-networks.

The core network and/or the access network(s) of the telecommunicationsnetwork include various network equipment that facilitate communicationsbetween UEs. The network equipment are located in various regions andare initially configured to provide optimal network performance given aset of network conditions assumed to be present in the respectiveregions. However, various factors (e.g., the introduction of additionalor different types of UEs, network interference, changing user networkusage habits, damage to network equipment, etc.) can contribute to theactual network conditions in a particular region differing from what wasexpected. Thus, some network equipment may be misconfigured and unableto provide optimal network performance.

BRIEF DESCRIPTION OF DRAWINGS

Throughout the drawings, reference numbers may be re-used to indicatecorrespondence between referenced elements. The drawings are provided toillustrate example embodiments described herein and are not intended tolimit the scope of the disclosure.

FIG. 1 is a block diagram of an illustrative network equipment operationadjustment environment in which a network equipment operation adjustmentsystem generates one or more network scores, generates user interfacedata for displaying the network score(s), and uses the network score(s)to control network equipment and/or components in an access network.

FIG. 2 is a block diagram of the network equipment operation adjustmentenvironment of FIG. 1 illustrating the operations performed by thecomponents of the network equipment operation adjustment environment togenerate KPIs, according to one embodiment.

FIG. 3A is a block diagram of the network equipment operation adjustmentenvironment of FIG. 1 illustrating the operations performed by thecomponents of the network equipment operation adjustment environment togenerate a network score, according to one embodiment.

FIG. 3B is a block diagram of the network equipment operation adjustmentenvironment of FIG. 1 illustrating the operations performed by thecomponents of the network equipment operation adjustment environment togenerate user interface data that, when executed, causes a user deviceto display a user interface depicting one or more network scores,according to one embodiment.

FIG. 3C is a block diagram of the network equipment operation adjustmentenvironment of FIG. 1 illustrating the operations performed by thecomponents of the network equipment operation adjustment environment toautomatically reconfigure network equipment, according to oneembodiment.

FIGS. 4A-4G illustrate a user interface that displays network scoreand/or KPI value information in various configurations to optimize thescreen space given the finite amount of space available to displayinformation.

FIG. 5 is a flow diagram depicting a network equipment adjustmentroutine illustratively implemented by a network equipment operationadjustment system, according to one embodiment.

DETAILED DESCRIPTION

As described above, network equipment in one or more regions may bemisconfigured and unable to provide optimal network performance. Networkequipment, UEs, and/or other components in a core network and/or accessnetwork(s) of the telecommunications network can monitor networkconditions, storing such data in a data store. For example, monitorednetwork conditions can include information identifying a type of datatechnology capability used by a UE (e.g., 3G, 4G, LTE, etc.), whatpercentage of the total data usage in a session was at the highest datatechnology capability of a UE (e.g., if the UE is LTE capable, whatpercentage of the total data usage in a session was at LTE rather thanat lower technology capabilities like 3G, 4G, etc.), how long a UE datasession lasted, the data transfer rate or throughput of data transferredto and/or from a UE, whether a phone call received or initiated by a UEwas dropped, the length of a phone call received or initiated by a UE,whether there was any interference in a signal transmitted to and/orfrom a UE, a strength of a signal between a UE and a cellular tower,etc. The monitored network conditions are generally associated orobtained from a single UE or a group of UEs in communication with eachother.

A separate system, such as a key performance indicator (KPI) generatorsystem, can retrieve the network conditions data stored in the datastore and use the network conditions data to generate a set of KPIs thateach indicate how a telecommunications network is performing. Inparticular, the KPIs can represent how the network is performing in theaggregate (e.g., taking into account some or all of the monitorednetwork conditions associated or obtained from UEs). For example, KPIscan include voice accessibility (e.g., the ability of a user using avoice service to access the telecommunications network), a voice dropcall rate (e.g., a percentage of all voice-based phone calls that aredropped over a certain time period), a session initiation protocol (SIP)drop call rate (e.g., a percentage of all Internet protocol (IP)network-based phone calls that are dropped over a certain time period),a combined drop call rate (e.g., a percentage of all voice-based and IPnetwork-based phone calls that are dropped over a certain time period),UE downlink throughput (e.g., a data transfer rate supported by thetelecommunications network for communications transmitted to UEs), UEuplink throughput (e.g., a data transfer rate supported by thetelecommunications network for communications transmitted from UEs),leakage (e.g., a percentage of time that a UE with higher technologycapabilities (e.g., LTE) is using lower technology capabilities (e.g.,3G, 4G, etc.)), network interference (e.g., signal to interference andnoise ratio (SINR) for physical uplink shared channel (PUSCH), apercentage of cell sectors in a region that are congested, etc.),average signal strength of signals received by UEs, total voice traffic,total data traffic, and/or other data points that describe technical,operational attributes of a telecommunications network.

The KPI generator system can generate KPIs for one or more geographicregions. For example, network conditions can be monitored in a pluralityof geographic regions, and the network conditions monitored in onegeographic region can be used to generate a set of KPIs for thatgeographic region. Thus, the KPIs of a first geographic region can beanalyzed to assess the network performance within the first geographicregion.

Typically, a network operator could retrieve the KPIs for a specificgeographic region, identify a KPI that has poor results (e.g., identifythat the voice drop call rate is high), and adjust the operation ofnetwork equipment within the geographic region to improve the poorlyperforming KPI. However, this approach may not yield the desiredresults. For example, some or all KPIs are inter-dependent. In otherwords, the value of one KPI may affect the value of another KPI. Thus,improving one KPI may have the unintended consequence of degradinganother KPI. There may be hundreds of KPIs, and so it may be difficultto discern which KPIs are inter-dependent and/or how changing one KPImay affect another. Conventional systems provide no mechanism forautomatically identifying an adjustment to one or more network equipmentparameters that would result in an improvement to the overallperformance of a telecommunications network and suggesting and/orapplying this adjustment.

Accordingly, described herein is a network equipment operationadjustment system configured to improve the performance of atelecommunications network by generating a network score representingthe performance of a telecommunications network within a geographicregion, determining one or more network equipment parameter adjustmentsusing the network score, and causing the adjustments to occur. Thenetwork equipment operation adjustment system can further display thenetwork score and other network scores for other geographic regions inan interactive user interface to efficiently allow a network operator toview the network performance of a telecommunications network bygeographic region and/or to view how the network performance in each ofthe geographic regions is changing over time.

The foregoing aspects and many of the attendant advantages of thisdisclosure will become more readily appreciated as the same becomebetter understood by reference to the following detailed description,when taken in conjunction with the accompanying drawings.

Example Network Equipment Operation Adjustment Environment

FIG. 1 is a block diagram of an illustrative network equipment operationadjustment environment 100 in which a network equipment operationadjustment system 140 generates one or more network scores, generatesuser interface data for displaying the network score(s), and uses thenetwork score(s) to control network equipment 130 and/or components inan access network 120. The environment 100 includes a core network 110,one or more UEs 102 that communicate with the core network 110 via theaccess network 120, and one or more end user devices 104 thatcommunicate with the core network 110 via the access network 120. Thecore network 110 includes the network equipment 130, the networkequipment operation adjustment system 140, and a KPI generator system150.

The UE 102 can be any computing device, such as a desktop, laptop ortablet computer, personal computer, wearable computer, server, personaldigital assistant (PDA), hybrid PDA/mobile phone, electronic bookreader, appliance (e.g., refrigerator, washing machine, dryer,dishwasher, etc.), integrated component for inclusion in computingdevices, home electronics (e.g., television, set-top box, receiver,etc.), vehicle, machinery, landline telephone, network-based telephone(e.g., voice over Internet protocol (“VoIP”)), cordless telephone,cellular telephone, smart phone, modem, gaming device, media device,control system (e.g., thermostat, light fixture, etc.), and/or any othertype of Internet of Things (IoT) device or equipment. In an illustrativeembodiment, the UE 102 includes a wide variety of software and hardwarecomponents for establishing communications over one or morecommunication networks, including the access network 120, the corenetwork 110, and/or other private or public networks. For example, theUE 102 may include a subscriber identification module (SIM) card (e.g.,an integrated circuit that stores data to identify and authenticate a UEthat communicates over a telecommunications network) and/or othercomponent(s) that enable the UE 102 to communicate over the accessnetwork 120, the core network 110, and/or other private or publicnetworks via a radio area network (RAN) and/or a wireless local areanetwork (WLAN). The SIM card may be assigned to a particular useraccount.

The end user devices UE 104 can also each be any computing device, suchas a desktop, laptop or tablet computer, personal computer, wearablecomputer, server, personal digital assistant (PDA), hybrid PDA/mobilephone, electronic book reader, appliance, integrated component forinclusion in computing devices, home electronics, vehicle, machinery,landline telephone, network-based telephone, cordless telephone,cellular telephone, smart phone, modem, gaming device, media device,control system, and/or any other type of IoT device or equipment. In anillustrative embodiment, the end user devices 104 include a wide varietyof software and hardware components for establishing communications overone or more communication networks, including the access network 120,the core network 110, and/or other private or public networks. However,while some end user devices 104 may include the same communicationcomponents as the UE 102, other end user devices 104 may not include aSIM card or other component(s) that enables the respective end userdevice 104 to communicate over the access network 120, the core network110, and/or other private or public networks via a RAN. Rather, the enduser devices 104 may be configured to communicate over the accessnetwork 120, the core network 110, and/or other private or publicnetworks via a WLAN.

The UEs 102 and/or end user devices 104 are communicatively connected tothe core network 110 via the access network 120, such as GSM EDGE RadioAccess Network (GRAN), GSM EDGE Radio Access Network (GERAN), UniversalTerrestrial Radio Access Network (UTRAN), Evolved Universal TerrestrialRadio Access (E-UTRAN), and/or the like. Illustratively, the accessnetwork 120 is distributed over land areas called cells, each served byat least one fixed-location transceiver, known as a cell site or basestation. The base station provides the cell with the network coveragewhich can be used for transmission of voice, messages, or other data. Acell might use a different set of frequencies from neighboring cells, toavoid interference and provide guaranteed service quality within eachcell. When joined together these cells provide radio coverage over awide geographic area. This enables a large number of UEs 102 and/or enduser devices 104 to communicate via the fixed-location transceivers.Although the access network 120 is illustrated as a single network, oneskilled in the relevant art will appreciate that the access network canbe include any number of public or private communication networks and/ornetwork connections.

The core network 110 provides various services to UEs 102 and/or enduser devices 104 that are connected via the access network 120. One ofthe main functions of the core network 110 is to route telephone calls,messages, and/or other data across a public switched telephone network(PSTN) or Internet protocol (IP) Multimedia Subsystem (IMS). Forexample, the core network 110 may include a call routing system, whichroutes telephone calls, messages, and/or other data across a PSTN orIMS. The network equipment 130 may include components of the PSTN,components of the IMS, the call routing system, and/or other physicalcomponents or computing devices that enable the routing of telephonecalls, messages, and/or other data. The core network 110 may providehigh capacity communication facilities that connect various nodesimplemented on one or more computing devices, allowing the nodes toexchange information via various paths.

Certain core network 110 nodes may be associated with the networkequipment operation adjustment system 140 and other core network 110nodes may be associated with the KPI generator system 150.Alternatively, not shown, the network equipment operation adjustmentsystem 140 and/or the KPI generator system 150 can be located externalto the core network 110 (e.g., in another network that can access thecore network 110 to retrieve desired data, such as network performancedata or KPI data).

The KPI generator system 150 can retrieve network performance data fromnetwork performance data store 170 to generate one or more KPIs for oneor more geographic regions. For example, the network performance datacan include network conditions monitored by UEs 102 (e.g., networkconditions monitored by a network application running on the UE 102, bya third party application running on the UE 102, etc.), components ofthe access network 120 (e.g., a cell site, a base station, a cellulartower, etc.), and/or by the network equipment 130. The networkperformance data can be stored in the network performance data store 170in entries associated with a particular geographic region. Thus, the KPIgenerator system 150 can generate one or more KPIs for a firstgeographic region by retrieving the network performance datacorresponding to the first geographic region. The KPI generator system150 can generate a KPI by aggregating, processing, and/or performingmathematical operations on some or all of the retrieved networkperformance data. As an illustrative example, the KPI generator system150 can generate a voice drop call rate for a first period of time for afirst geographic region by retrieving the network performance datacorresponding to the first geographic region, using the retrievednetwork performance data to identify a number of voice calls made by UEs102 during the first period of time and a number of times the voicecalls were dropped. The KPI generator system 150 can then divide thenumber of times the voice calls were dropped by the number of voicecalls made by UEs 102 during the first period of time to generate thevoice drop call rate. The KPI generator system 150 can periodicallygenerate KPIs (e.g., in periodic cycles, when new network performancedata is available in the network performance data store 170, when arequest is received from an end user device 104, etc.).

Once the KPI(s) are generated for a first geographic region, the KPIgenerator system 150 can store the KPI(s) in KPI data store 160. The KPIgenerator 150 can then repeat this process for other geographic regions.Generally, KPIs correspond with a certain period of time (e.g., a pastminute, a past hour, a past day, a past week, a past month, etc.). Thus,the KPI generator system 150 can store a KPI in the KPI data store 160in an entry associated with the period of time to which the KPIcorresponds and the geographic region to which the KPI corresponds.Accordingly, over time, the KPI data store 160 includes entriesrepresenting the history of KPI values.

As illustrated in FIG. 1, the network equipment operation adjustmentsystem 140 may include several components and/or data repositories, suchas a network score generator 142, a network equipment controller 144, auser interface generator 146, a region KPI goal data store 147, and anetwork score data store 149. In an embodiment, the network scoregenerator 142 generates a network score for a geographic region thatrepresents the overall performance of the telecommunications network inthat geographic region. The network score generator 142 can generate anetwork score for each of a plurality of geographic regions.

For example, a network score generated by the network score generator142 for a geographic region can be based on how well the KPIs in thegeographic region compare to the KPI goals of the geographic region(represented by a region score), how well the KPIs in the geographicregion compare to the corresponding KPIs in other geographic regions(represented by a national score), and/or extra data that may indicate“good behavior” by the telecommunications network in the geographicregion (represented by an extra score). The network score generator 142can combine a region score for the geographic region, a national scorefor the geographic region, and an extra score for the geographic regionto form the network score for the geographic region.

In particular, a KPI goal can be a KPI value that the telecommunicationsnetwork in a geographic region is trying to achieve. As an illustrativeexample, one KPI may be UE 102 uplink throughput. The KPI goal for thisKPI in a first geographic region may be 3.0 Mb/s, meaning that a goal ofthe telecommunications network in the first geographic region is toachieve a total or average UE 102 uplink throughput of greater than 3.0Mb/s during a certain period of time (e.g., a past minute, a past hour,a past day, a past week, a past month, etc.).

Accordingly, to determine the region score for a geographic region, thenetwork score generator 142 can retrieve KPI goal data (e.g., a set ofKPI goals for various KPIs) for the geographic region from the regionKPI goal data store 147 and KPI data (e.g., a set of current KPI valuesfor various KPIs) for the geographic region from the KPI data store 160.For each KPI, the network score generator 142 can compare the currentvalue of the respective KPI with the KPI goal corresponding to therespective KPI. If the comparison yields a result indicating that thecurrent KPI value is better than the KPI goal (e.g., the current KPIvalue is greater than the KPI goal for KPIs like UE 102 uplinkthroughput, voice accessibility, etc., and is less than the KPI goal forKPIs like voice drop call rate, leakage, PUSCH SINR, etc.), then thenetwork score generator 142 can increase a region score of thegeographic region by 1 (or any other numerical value). On the otherhand, if the comparison yields a result indicating that the current KPIvalue is worse than the KPI goal, then the network score generator 142can leave the region score of the geographic region unchanged (ordecrease the region score of the geographic region by 1 or any othernumerical value). Thus, the network score generator 142 can iteratethrough each KPI, incrementing the region score of the geographic regionby 1 (or any other numerical value) each time a current KPI value isbetter than the KPI goal.

To determine the national score, the network score generator 142 canretrieve KPI data for some or all geographic regions from the KPI datastore 160. For each KPI, the network score generator 142 can rank thegeographic regions by current KPI value of the respective KPI. As anillustrative example, if a first geographic region has a voice drop callrate of 0.6%, a second geographic region has a voice drop call rate of0.4%, and a third geographic region has a voice drop call rate of 0.7%,then the network score generator 142 can rank the second geographicregion first (e.g., because the second geographic region has the lowestvoice drop call rate), the first geographic region second, and the thirdgeographic region third. Thus, the network score generator 142 cangenerate N geographic region rankings, where N corresponds to the numberof KPIs that are being evaluated.

For each ranking, the network score generator 142 then assigns rankingscores to the geographic regions in the respective ranking. For example,a geographic region with the highest rank in one ranking is assigned ahighest ranking score (e.g., a score corresponding to the number ofgeographic regions that are ranked), a geographic region with the lowestranking in the one ranking is assigned a lowest ranking score (e.g., 0),and the remaining geographic regions are assigned ranking scores inbetween the highest and lowest ranking scores that are dependent on therespective geographic region's rank. The network score generator 142 canthen sum all of the ranking scores assigned to a particular geographicregion across the different rankings to determine the national score forthat geographic region. As an illustrative example, if N is 3 and afirst geographic region is assigned a ranking score of 1 in a firstranking (e.g., corresponding to a first KPI), a ranking score of 4 in asecond ranking (e.g., corresponding to a second KPI), and a rankingscore of 7 in a third ranking (e.g., corresponding to a third KPI), thenthe national score for the first geographic region is 12.

As another example, the network score generator 142, for each ranking,identifies an average KPI value of the geographic regions that areranked. The network score generator 142 then assigns a ranking score of1 (or any other numerical value) to each of the geographic regions inthe respective ranking that have a current KPI value greater than (orequal to) the average KPI value and a ranking score of 0 (or any othernumerical value) to each of the geographic regions in the respectiveranking that have a current KPI value less than (or equal to) theaverage KPI value. The network score generator 142 can then sum all ofthe ranking scores assigned to a particular geographic region across thedifferent rankings to determine the national score for that geographicregion. As an illustrative example, if a first geographic region isassigned a ranking score of 1 in a first ranking (e.g., because thecurrent KPI value of the first geographic region is greater than theaverage KPI value of a first KPI corresponding to the first ranking), aranking score of 0 in a second ranking (e.g., because the current KPIvalue of the first geographic region is less than the average KPI valueof a second KPI corresponding to the second ranking), and a rankingscore of 1 in a third ranking (e.g., because the current KPI value ofthe first geographic region is greater than the average KPI value of athird KPI corresponding to the third ranking), then the national scorefor the first geographic region is 2.

To determine the extra score, the network score generator 142 canidentify a set of metrics that represent “good behavior” by a network ina geographic region. For example, such metrics can include whether acertain application is run by a threshold percentage of UEs 102 in ageographic region, whether data measured by a third party application(e.g., data throughput) running on UEs 102 in a geographic regionexceeds a threshold value in the aggregate, whether signal coverage ofone or more base stations in a geographic region exceeds a thresholdarea, whether network interference in a geographic region has decreasedover a certain period of time by a threshold percentage or amount,whether a threshold percentage or portion of the network in a geographicregion is a self organizing network (SON) (e.g., a network thatconfigures itself to optimize performance), and/or other metrics thatmay indicate that network performance in the geographic region isexceeding user and/or network operator expectations. The metrics maydefine a single score tier (e.g., a 1 if the metric is met, a 0 if themetric is not met) or multiple score tiers (e.g., a 1 if a baselineversion of the metric is met, a 2 if an enhanced version of the metricis met, a 0 if the metric is not met, etc.).

The network score generator 142 can then process, for a geographicregion, the KPI data, the KPI goal data, and/or other metric dataobtained from an external source (not shown) to determine the extrascore. For example, the network score generator 142 can increment theextra score for a geographic region by 1 (or any other numerical value,such as greater values if the metric defines multiple score tiers) eachtime the processed data indicates that the geographic region complieswith a metric. As an illustrative example, if a metric indicates that70% application usage results in a 1, whereas 80% application usageresults in a 2, 71% of the UEs 102 located in a first geographic regionuse an application, 85% of the UEs 102 located in a second geographicregion use the application, and 69% of the UEs 102 located in a thirdgeographic region use the application, then the extra score for thefirst geographic region can be incremented by 1, the extra score for thesecond geographic region can be incremented by 2, and the extra scorefor the third geographic region can remain unchanged. Thus, the networkscore generator 142 can iterate through each metric, incrementing theextra score of a geographic region by 1 (or any other numerical value)each time the geographic region complies with the respective metric.

For each geographic region, the network score generator 142 sums theregion score of the respective geographic region, the national score ofthe respective geographic region, and the extra score of the respectivegeographic region to determine the network score of the respectivegeographic region. The network score generator 142 then stores thegenerated network scores in the network score data store 149. Eachnetwork score can be stored in association with a geographic region anda time or time period corresponding to the time or time period of theKPIs used to generate the network score.

The network equipment controller 144 can be configured to use one ormore network scores in order to adjust the operation of a networkequipment 130 and/or a component in the access network 120 (e.g., a cellsite, a base station, a cellular tower, etc.). For example, the networkequipment controller 144 can use machine learning to determine anadjustment to the operation of a network equipment 130 and/or acomponent in the access network 120. In particular, the networkequipment controller 144 can train a machine learning model thatidentifies a network equipment 130 or access network 120 component toreconfigure and/or one or more parameters to adjust. The networkequipment controller 144 can train the machine learning model ontraining data that includes information identifying a past network scoreof a geographic region, past KPI value(s) for one or more KPI(s) of thegeographic region, what network equipment 130 and/or access network 120components were reconfigured in the geographic region, what parameter(s)were changed and how were those parameter(s) changed to implement thereconfiguration, and/or how did the change affect the network score ofthe geographic region going forward. Optionally, the training data canfurther include trend information (e.g., how the network score and/orthe KPI value(s) have changed over a period of time). The machinelearning model can be trained to identify adjustments that would resultin an improved network score, which would result in an improved networkperformance.

To determine an adjustment, the network equipment controller 144 canretrieve one or more network scores for a geographic region from thenetwork score data store 149 and one or more KPI value(s) from the KPIdata store 160 (e.g., a single KPI value for each of a plurality ofKPIs, a set of KPI values for each of a plurality of KPIs (e.g., a setof historical KPI values), etc.). The network equipment controller 144can then apply the retrieved network score(s) and/or the retrieved KPIvalue(s) as inputs to the machine learning model. As a result, themachine learning model may produce an identification of a networkequipment 130 and/or access network 120 component to reconfigure and/orone or more parameters to adjust and what the adjustment should be. Inresponse, the network equipment controller 144 can transmit aninstruction to the identified network equipment 130 and/or accessnetwork 120 component that identifies one or more parameters to adjustand what the adjustment should be. Receipt of the instruction causes thereceiving component to implement indicated parameter adjustment. Thus,the network score generated by the network score generator 142 can beused to physically alter the operation of physical network equipment toimprove the performance of a telecommunications network within ageographic region.

As an illustrative example, the result produced by the machine learningmodel may indicate that the signal strength of a cellular tower shouldbe increased by 10%. Thus, the network equipment controller 144 cantransmit an instruction to the cellular tower to increase signalstrength by 10%. Receipt of the instruction may cause the cellular towerto increase the power of the signal transmission (e.g., by 10% oranother percent value that corresponds with a signal strength increaseof 10%) such that the signal strength increases by 10%.

In some embodiments, the network equipment controller 144 can transmitinstructions to physical network equipment to improve the networkperformance without using a machine learning model. For example, anetwork operator can access the network equipment operation adjustmentsystem 140 (e.g., via an end user device 104) and view network scoreand/or KPI information for a geographic region in a user interface(e.g., as described in greater detail below). Based on the viewedinformation, the network operator can identify changes to the way theaccess network 120 and/or the core network 110 operate to improvenetwork performance (e.g., change network equipment 130 or accessnetwork 120 component parameters, such as by changing the direction inwhich signals are transmitted, changing the signal strength, changingthe ratio of resources allocated to voice and data traffic, etc., addnew cellular towers, etc.) and instruct the network equipment controller144 to transmit an instruction constructed by the network operator basedon the identified changes.

The user interface generator 146 can retrieve one or more network scoresfrom the network score data store 149 in order to generate userinterface data that, when executed by an end user device 104, causes theend user device 104 to display an interactive user interface thatdisplays the network scores and/or other information. The interactiveuser interfaces are described in greater detail below with respect toFIGS. 4A-4G.

The network equipment operation adjustment system 140 and/or the KPIgenerator system 150 may be a single computing device or may includemultiple distinct computing devices, such as computer servers, logicallyor physically grouped together to collectively operate as a serversystem. The components of the network equipment operation adjustmentsystem 140 and/or the KPI generator system 150 can each be implementedin application-specific hardware (e.g., a server computing device withone or more ASICs) such that no software is necessary, or as acombination of hardware and software. In addition, the modules andcomponents of the network equipment operation adjustment system 140and/or the KPI generator system 150 can be combined on one servercomputing device or separated individually or into groups on severalserver computing devices. In some embodiments, the network equipmentoperation adjustment system 140 and/or the KPI generator system 150 mayinclude additional or fewer components than illustrated in FIG. 1.

The region KPI goal data store 147 stores KPI goal data for a pluralityof KPIs and for a plurality of geographic regions. While the region KPIgoal data store 147 is depicted as being located internal to the networkequipment operation adjustment system 140, this is not meant to belimiting. For example, in some embodiments not shown, the region KPIgoal data store 147 is located external to the network equipmentoperation adjustment system 140.

The network score data store 149 stores network scores for a pluralityof geographic regions. The network score generator 142 can periodicallygenerate a new network score for each geographic region (e.g., eachhour, each day, each week, each month, etc.) and store the new networkscore in the network score data store 149. Thus, the network score datastore 149 can include a plurality of network scores for a singlegeographic region. While the network score data store 149 is depicted asbeing located internal to the network equipment operation adjustmentsystem 140, this is not meant to be limiting. For example, in someembodiments not shown, the network score data store 149 is locatedexternal to the network equipment operation adjustment system 140.

The KPI data store 160 stores KPI data for a plurality of KPIs and for aplurality of geographic regions. While the KPI data store 160 isdepicted as being located external to the network equipment operationadjustment system 140 and the KPI generator system 150, this is notmeant to be limiting. For example, in some embodiments not shown, theKPI data store 160 is located internal to the network equipmentoperation adjustment system 140 and/or the KPI generator system 150.

The network performance data store 170 stores network performance datafor a plurality of geographic regions. While the network performancedata store 170 is depicted as being located external to the networkequipment operation adjustment system 140 and the KPI generator system150, this is not meant to be limiting. For example, in some embodimentsnot shown, the network performance data store 170 is located internal tothe network equipment operation adjustment system 140 and/or the KPIgenerator system 150.

Example Block Diagram for Generating KPIs

FIG. 2 is a block diagram of the network equipment operation adjustmentenvironment 100 of FIG. 1 illustrating the operations performed by thecomponents of the network equipment operation adjustment environment 100to generate KPIs, according to one embodiment. As illustrated in FIG. 2,the network equipment 130 monitors network performance of one or moreUEs at (1). The network equipment 130 stores network performance datacorresponding to the monitoring in the network performance data store170 at (2). The network equipment 130 can store the network performancedata in an entry associated with the geographic region(s) in which theUEs 102 are located.

Before, during, or after the network equipment 130 monitors the networkperformance of the one or more UEs 102, one or more UEs 102 can obtainnetwork performance data using a third party application running on theUE(s) 102 at (3). As an illustrative example, a third party applicationmay be configured to measure the uplink and/or downlink throughput ofthe network in the geographic region in which the UE 102 running thethird party application is located. The third party application can thenstore the obtained network performance data in the network performancedata store 170 at (4).

Optionally, other components, such as components in the access network120, can monitor, generate, or otherwise obtain network performancedata. Such components can store the network performance data in thenetwork performance data store 170.

The KPI generator system 150 can retrieve the network performance dataat (5). The KPI generator system 150 can then generate one or more KPIsusing the network performance data at (6). Once the KPI(s) aregenerated, the KPI generator system 150 stores the generated KPI(s) inthe KPI data store 160 at (7). As described herein, the generated KPI(s)can be stored in association with a geographic region corresponding tothe network performance data used to generate the KPI(s).

Example Block Diagram for Generating and Using Network Scores

FIG. 3A is a block diagram of the network equipment operation adjustmentenvironment 100 of FIG. 1 illustrating the operations performed by thecomponents of the network equipment operation adjustment environment 100to generate a network score, according to one embodiment. As illustratedin FIG. 3A, the network score generator 142 can retrieve KPI goal datafor a first region at (1). For example, a region or geographic regioncan include a cellular site, a city, a county, a state, etc.

The network score generator 142 can further retrieve KPI data for thefirst region and one or more other regions at (2). The network scoregenerator 142 can then generate a network score for the first regionusing the retrieved KPI goal data and the KPI data at (3). For example,the network score generator 142 can use the KPI data for the firstregion in conjunction with the KPI data for the other regions todetermine a region score for the first region. The network scoregenerator 142 can use the KPI data for the first region and the otherregions to determine a national score for the first region. Optionally,the network score generator 142 can retrieve other metric data to use inconjunction with the KPI data to determine an extra score for the firstregion. The network score generator 142 can then sum the region score,the national score, and the extra score to generate the network scorefor the first region. The network score generator 142 can then store thenetwork score for the first region in the network score data store 149at (4).

In some embodiments, the network score generator 142 can repeat theoperations described above to generate a network score for each of aplurality of regions. The network score generator 142 can store thesenetwork scores in the network score data store 149. In addition, thenetwork score generator 142 can repeat these operations periodically forone or more regions, thereby generating multiple network scores for agiven region.

FIG. 3B is a block diagram of the network equipment operation adjustmentenvironment 100 of FIG. 1 illustrating the operations performed by thecomponents of the network equipment operation adjustment environment 100to generate user interface data that, when executed, causes a userdevice to display a user interface depicting one or more network scores,according to one embodiment. As illustrated in FIG. 3B, the userinterface generator 146 retrieves network scores for a plurality ofregions from the network score data store 149 at (1).

The user interface generator 146 then generates user interface datathat, when executed, causes a user device (e.g., an end user device 104)to display a user interface depicting visualizations of the retrievednetwork scores at (2). For example, user interfaces have a finite amountof space, and so it can be difficult to display a large amount of datagiven the finite amount of space. This can be especially problematic insituations in which a network operator or other user desires to view notonly the network scores, but also other information, such as networkscore trends, what KPIs are positively and/or negatively impactingnetwork scores, how network scores and/or KPI values differ region byregion, and/or the like. Thus, the user interface displayed as a resultof executing the user interface data generated by the user interfacegenerator 146 may be configured in a manner to overcome the sizeconstraints presented by a typical user interface. Additional details ofthe user interface are described below with respect to FIGS. 4A-4G.

The user interface generator 146 can transmit the user interface data tothe end user device 104 at (3). The end user device 104 can then executethe user interface data to display the user interface at (4).

FIG. 3C is a block diagram of the network equipment operation adjustmentenvironment 100 of FIG. 1 illustrating the operations performed by thecomponents of the network equipment operation adjustment environment 100to automatically reconfigure network equipment, according to oneembodiment. As illustrated in FIG. 3C, the network equipment controller144 retrieves one or more network scores for a first region from thenetwork score data store 149 at (1). In some embodiments, the networkequipment controller 144 further retrieves one or more KPI valuesassociated with the first region. In further embodiments, the networkequipment controller 144 retrieves a set of historical network scores(e.g., network scores generated over the past 7 days) and/or a set ofhistorical KPI values (e.g., KPI values generated over the past 7 days).

The network equipment controller 144 determines a modification to aparameter of a first network equipment for possibly improving thenetwork performance in the first region at (2). For example, the networkequipment controller 144 can use the retrieved network score(s) and/orretrieved KPI value(s) to determine the parameter modification. In someembodiments, the network equipment controller 144 executes a machinelearning model, using the retrieved data as an input to the model, toidentify the first network equipment to reconfigure and the parametermodification.

The network equipment controller 144 then transmits an instruction tothe first network equipment 130 to modify the parameter at (3). Inresponse, the network equipment 130 adjusts its operation by modifyingthe parameter at (4).

Alternatively or in addition, the network equipment controller 144identifies an access network 120 component to reconfigure and transmitsthe instruction to the identified component. In response, the accessnetwork 120 component adjusts its operation according to theinstruction.

Example Network Score User Interfaces

FIGS. 4A-4G illustrate a user interface 400 that displays network scoreand/or KPI value information in various configurations to optimize thescreen space given the finite amount of space available to displayinformation. The user interface 400 can be displayed by the end userdevice 104 and user interface data that causes the end user device 104to display the user interface 400 can be generated by the user interfacegenerator 146 of the network equipment operation adjustment system 140.

As illustrated in FIG. 4A, the user interface 400 includes a window 405and a window 410. The window 405 includes a region button 411, anational button 412, an extra button 413, a rankings button 414, a KPIschart button 415, and a region chart button 416. Selection of the regionbutton 411, as depicted in FIG. 4A, causes the window 410 to identify aplurality of regions, where each region occupies a different column inthe window 410. Within each column, the network score is depicted forthe region corresponding to the respective column (e.g., as displayed inshapes 417-420), network score trend data is depicted (e.g., via thearrows depicted in the shapes 417-420), current KPI values are depictedfor the region corresponding to the respective column, KPI value trenddata is depicted (e.g., whether the KPI values are trending upward,trending downward, remaining stable, etc.), KPI goal data is depictedfor the region corresponding to the respective column, informationindicating whether KPI goal data is met, and a region score is depictedfor the region corresponding to the respective column.

As described herein, the user interface 400 has a finite amount ofspace. However, the number of KPIs for each region may be large. Thus,in order to depict all of the information described above in the window410, the user interface 400 depicts such information in a compactformat. For example, the window 410 includes a matrix, where the columnscorrespond to different regions and the rows correspond to differentKPIs. As depicted in FIG. 4A, the first row corresponds to KPI 1, thesecond row corresponds to KPI 2, the third row corresponds to KPI 3, andso on. Likewise, the first column corresponds to region 1, the secondcolumn corresponds to region 2, the third column corresponds to region4, and so on.

Within each element of the matrix, a shape is displayed (e.g., a circle,a square, a rectangle, etc.), where the shape corresponds to a regionand KPI. Within each shape, the window 410 displays a numerical valuerepresenting the current KPI value of the corresponding region and KPI.For example, shape 421 is a circle corresponding to region 1 and KPI 1.The numerical value 0.56 in the shape 421 represents the current KPIvalue of KPI 1 in region 1.

KPI goal data is depicted below the shapes, such as a threshold KPIvalue and whether the goal is to exceed or not exceed the threshold KPIvalue. For example, “<0.50%” is depicted below the shape 421, indicatingthat the threshold KPI value is 0.50% and the goal is to be less than0.50%. The shapes are further shaded to indicate whether the KPI goal ismet. For example, shapes are shaded a dark color if the KPI goal is metand a light color if the KPI goal is not met, or vice-versa. The goalfor KPI 1 in region 1 was not met (e.g., 0.56% is greater, not lessthan, 0.50%) and thus the shape 421 is shaded a light color. On theother hand, the goal for KPI 2 in region 2 was met (e.g., 99.9% isgreater, not less than, 99.85%) and thus shape 422 is shaded a darkcolor.

The numerical values within the shapes may overlay an arrow thatindicates the direction in which the respective KPI values have changedover a period of time (e.g., an hour, a day, a week, a month, etc.). Forexample, the shapes 421 and 422 includes a numerical value laying overan up arrow, indicating that KPI 1 in region 1 and KPI 2 in region 2 areboth trending upwards. However, shape 423 includes a numerical valuelaying over a down arrow, indicating that KPI 1 in region 3 is trendingdownwards, and shape 424 includes a numerical value laying over asideways arrow, indicating that KPI 1 in region 2 has remained stable.

In some embodiments, the arrows are not initially depicted in theshapes. Rather, when a user moves a cursor over a shape (e.g., hoversover a shape with a cursor) or otherwise selects the shape, then thearrow appears within the shape. Thus, the user interface 400 can depictthe trend information in a compact format without adding additionalinformation to another portion of the user interface 400, causing a newwindow to pop-up to display the trend information, and/or the like.

Selection of the national button 412 causes the window 410 to depict atable 430 identifying a plurality of regions and the KPI rankings foreach region, as illustrated in FIG. 4B. The window 410 further depictsthe national score for each region. As depicted in the table 430, region1 is ranked lower for KPI 1 and KPI 3, and has a middle rank for KPI 2.On the other hand, region 2 is ranked higher for KPIs 1, 2, and 3.Region 2 may have a higher overall ranking than region 1 because thecurrent KPI 1, 2, and 3 values for region 2 are better (e.g., higher,like for throughput, or lower, like for voice drop call rate) than thecurrent KPI 1, 2, and 3 values for region 1. Similarly, region 3 isranked higher for KPI 2, and has a middle rank for KPIs 3 and 1. Region4 is ranked higher for KPIs 1 and 3, and has a lower rank for KPI 2.

Selection of the extra button 413 causes the window 410 to depict amatrix in which the columns represent different regions and the rowsrepresent various extra metrics, as illustrated in FIG. 4C. The window410 further depicts the extra score for each region. Within each matrixelement, the window 410 displays a shape indicating whether thecorresponding extra metric contributed to the extra score for thecorresponding region and, if so, by how much. For example, shape 440corresponds to region 1 and extra metric 1. The shape 440 depicts anumerical value of 0 and is lightly shaded, indicating that the region 1did not comply with the requirements of extra metric 1. On the otherhand, shape 441, which corresponds to region 2 and extra metric 1,depicts a numerical value of 2 and is darkly shaded, indicating that theregion 2 complied with one tier of the extra metric 1 (e.g., a tier,such as a higher threshold value, that if met, results in an incrementto the extra score by 2).

Selection of the rankings button 414 causes the window 410 to depict avertical bar graph 450 and a horizontal bar graph 452, as illustrated inFIG. 4D. The vertical bar graph 450 may depict, in bar graph form, thenetwork scores for various regions. For example, the vertical bar graph450 depicts the network scores for regions 1-4. While the regions andthe corresponding network scores depicted in shapes 417-420 depictednear the top of the window 410 may be organized in a set manner (e.g.,alphabetical, by location, etc.), the regions in the vertical bar graph450 may be organized from highest network score to lowest network score(e.g., from left to right). Thus, as the network score generator 142continues to generate network scores, the vertical bar graph 450 maydynamically change the order of regions to reflect the new networkscores and the corresponding updated ranking of regions by networkscore.

The horizontal bar graph 452 can depict trend information, identifyingregions that have experienced the largest network score increases over aperiod of time and the regions that have experienced the largest networkscore decreases over the same period of time. For example, thehorizontal bar graph 452 depicts region 2 as experiencing the largestnetwork score increase (e.g., 11 points over the period of time) andregion 1 as experiencing the largest network score decrease (e.g., 9points over the period of time).

As described herein, a user may desire to view KPI information that caninform the user of why the network score of a particular region is asdepicted and/or what KPIs are positively and/or negatively impacting thenetwork score. However, the user interface 400 has a finite amount ofspace. Thus, in order to depict the KPI information despite the sizeconstraints of the user interface 400, the user interface 400 providesfunctionality to display the KPI information in a space-efficientmanner. For example, selection of region 1 (e.g., via the selection ofthe shape 417) causes the window 410 to shade and/or highlight thevertical bar graph 450 and the horizontal bar graph 452 such thatnetwork score information corresponding to the selected region 1 is morevisible than the network score information corresponding to the other,unselected regions, as illustrated in FIG. 4E. Furthermore, selection ofregion 1 causes the window 410 to display a sub window 460. Within thesub window 460, additional graphs are displayed, including a line graph461, a horizontal bar graph 462, and a line graph 463.

The line graph 461 depicts the network score of the region 1 over aperiod of time (e.g., a week). The horizontal bar graph 462 depictsinformation indicating which KPIs have positively (or negatively)contributed to the region 1 network score over a period of time (e.g., aweek). For example, on 1/1, KPIs 1, 2, and 5 positively (or negatively)contributed to the 1/1 region 1 network score. However, on 1/3, only KPI1 positively (or negatively) contributed to the 1/3 region 1 networkscore. The line graph 463 depicts the values of various region 1 KPIsover a period of time (e.g., a week). Thus, the graphs 461-463 in thesub window 460 can provide a user with additional insight as to why anetwork score is as depicted and/or what KPIs are positively ornegatively contributing to the network score, and this information alongwith the network score information provided in vertical bar graph 450and horizontal bar graph 452 can all be depicted in a manner thatovercomes the size constraints of the user interface 400.

Selection of the KPIs chart button 415 causes the window 410 to displayKPI charts 470-473, as illustrated in FIG. 4F. For example, the KPIcharts 470-473 correspond with region 1, which is still selected. If,for example, region 2 is selected via the shape 418, then the window 410automatically updates to display KPI charts corresponding to theselected region 2.

The KPI charts 470-473 may depict KPI information for thetelecommunications network operated by the user of the user interface400. One or more KPI charts 470-473 may also display KPI information forother telecommunications networks operated by other users. For example,the KPI chart 470 includes a line 475 and a line 476. The line 475represents the KPI 1 value in region 1 for the telecommunicationsnetwork operated by the user of the user interface 400. The line 476,however, represents the KPI 1 value in region 1 for anothertelecommunications network operated by another user. Thus, the user cancompare the performance of the telecommunications network in region 1 tothe performance of other telecommunications networks in region 1 withinthe same KPI chart 470, thereby reducing the amount of space needed inthe user interface 400 to display content.

Selection of the region chart button 416 causes the window 410 todisplay KPI charts 480-483 that depict KPI goal data, as illustrated inFIG. 4G. For example, the KPI charts 480-483 correspond with region 1,which is still selected. If, for example, region 2 is selected via theshape 418, then the window 410 automatically updates to display KPIcharts corresponding to the selected region 2.

The KPI charts 480-483 may depict KPI information for thetelecommunications network operated by the user of the user interface400. In addition, each KPI chart 480-483 depicts the KPI goal for therespective KPI. For example, the KPI chart 480 includes a line 485 and aline 486. The line 485 represents the KPI 1 value in region 1 for thetelecommunications network operated by the user of the user interface400. The line 486 represents the KPI goal for KPI 1 in region 1. Asdepicted in the KPI chart 480, the KPI 1 value meets the KPI goal onsome days and does not meet the KPI goal on other days. Thus, the usercan compare the performance of the telecommunications network in region1 to the corresponding KPI goals within the same KPI chart 480, therebyreducing the amount of space needed in the user interface 400 to displaycontent.

Example Network Equipment Adjustment Routine

FIG. 5 is a flow diagram depicting a network equipment adjustmentroutine 500 illustratively implemented by a network equipment operationadjustment system, according to one embodiment. As an example, thenetwork equipment operation adjustment system 140 of FIG. 1 can beconfigured to execute the network equipment adjustment routine 500. Thenetwork equipment adjustment routine 500 begins at block 502.

At block 504, KPI goal data is retrieved for a first region. Forexample, the KPI goal data can be retrieved from the region KPI goaldata store 147.

At block 506, KPI data for the first region and one or more otherregions is retrieved. In further embodiments, metrics data for the firstregion is also retrieved.

At block 508, a network score for the first region is generated. Forexample, the network score can be generated using the KPI goal data, theKPI data, and/or the metrics data.

At block 510, an adjustment to an operational parameter is determinedusing the generated network score. For example, a machine learning modelcan be used to determine the operational parameter adjustment. Theadjustment may be to an operational parameter of a network equipment 130or an access network 120 component.

At block 512, an instruction is transmitted to a network equipment inthe first region that, when received, causes the network equipment toadjust an operational parameter according to the determined adjustment.After the instruction is transmitted, the network equipment adjustmentroutine 500 is complete, as shown at block 514.

Terminology

All of the methods and tasks described herein may be performed and fullyautomated by a computer system. The computer system may, in some cases,include multiple distinct computers or computing devices (e.g., physicalservers, workstations, storage arrays, cloud computing resources, etc.)that communicate and interoperate over a network to perform thedescribed functions. Each such computing device typically includes aprocessor (or multiple processors) that executes program instructions ormodules stored in a memory or other non-transitory computer-readablestorage medium or device (e.g., solid state storage devices, diskdrives, etc.). The various functions disclosed herein may be embodied insuch program instructions, or may be implemented in application-specificcircuitry (e.g., ASICs or FPGAs) of the computer system. Where thecomputer system includes multiple computing devices, these devices may,but need not, be co-located. The results of the disclosed methods andtasks may be persistently stored by transforming physical storagedevices, such as solid state memory chips or magnetic disks, into adifferent state. In some embodiments, the computer system may be acloud-based computing system whose processing resources are shared bymultiple distinct business entities or other users.

Depending on the embodiment, certain acts, events, or functions of anyof the processes or algorithms described herein can be performed in adifferent sequence, can be added, merged, or left out altogether (e.g.,not all described operations or events are necessary for the practice ofthe algorithm). Moreover, in certain embodiments, operations or eventscan be performed concurrently, e.g., through multi-threaded processing,interrupt processing, or multiple processors or processor cores or onother parallel architectures, rather than sequentially.

The various illustrative logical blocks, modules, routines, andalgorithm steps described in connection with the embodiments disclosedherein can be implemented as electronic hardware (e.g., ASICs or FPGAdevices), computer software that runs on computer hardware, orcombinations of both. Moreover, the various illustrative logical blocksand modules described in connection with the embodiments disclosedherein can be implemented or performed by a machine, such as a processordevice, a digital signal processor (DSP), an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA) orother programmable logic device, discrete gate or transistor logic,discrete hardware components, or any combination thereof designed toperform the functions described herein. A processor device can be amicroprocessor, but in the alternative, the processor device can be acontroller, microcontroller, or state machine, combinations of the same,or the like. A processor device can include electrical circuitryconfigured to process computer-executable instructions. In anotherembodiment, a processor device includes an FPGA or other programmabledevice that performs logic operations without processingcomputer-executable instructions. A processor device can also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration. Although described herein primarily with respect todigital technology, a processor device may also include primarily analogcomponents. For example, some or all of the rendering techniquesdescribed herein may be implemented in analog circuitry or mixed analogand digital circuitry. A computing environment can include any type ofcomputer system, including, but not limited to, a computer system basedon a microprocessor, a mainframe computer, a digital signal processor, aportable computing device, a device controller, or a computationalengine within an appliance, to name a few.

The elements of a method, process, routine, or algorithm described inconnection with the embodiments disclosed herein can be embodieddirectly in hardware, in a software module executed by a processordevice, or in a combination of the two. A software module can reside inRAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory,registers, hard disk, a removable disk, a CD-ROM, or any other form of anon-transitory computer-readable storage medium. An exemplary storagemedium can be coupled to the processor device such that the processordevice can read information from, and write information to, the storagemedium. In the alternative, the storage medium can be integral to theprocessor device. The processor device and the storage medium can residein an ASIC. The ASIC can reside in a user terminal. In the alternative,the processor device and the storage medium can reside as discretecomponents in a user terminal.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements or steps.Thus, such conditional language is not generally intended to imply thatfeatures, elements or steps are in any way required for one or moreembodiments or that one or more embodiments necessarily include logicfor deciding, with or without other input or prompting, whether thesefeatures, elements or steps are included or are to be performed in anyparticular embodiment. The terms “comprising,” “including,” “having,”and the like are synonymous and are used inclusively, in an open-endedfashion, and do not exclude additional elements, features, acts,operations, and so forth. Also, the term “or” is used in its inclusivesense (and not in its exclusive sense) so that when used, for example,to connect a list of elements, the term “or” means one, some, or all ofthe elements in the list.

Disjunctive language such as the phrase “at least one of X, Y, or Z,”unless specifically stated otherwise, is otherwise understood with thecontext as used in general to present that an item, term, etc., may beeither X, Y, or Z, or any combination thereof (e.g., X, Y, or Z). Thus,such disjunctive language is not generally intended to, and should not,imply that certain embodiments require at least one of X, at least oneof Y, and at least one of Z to each be present.

While the above detailed description has shown, described, and pointedout novel features as applied to various embodiments, it can beunderstood that various omissions, substitutions, and changes in theform and details of the devices or algorithms illustrated can be madewithout departing from the spirit of the disclosure. As can berecognized, certain embodiments described herein can be embodied withina form that does not provide all of the features and benefits set forthherein, as some features can be used or practiced separately fromothers. The scope of certain embodiments disclosed herein is indicatedby the appended claims rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

What is claimed is:
 1. A computer-implemented method comprising:retrieving key performance indicator (KPI) goal data for a firstgeographic region; determining a first score using the KPI goal data;retrieving KPI data for the first geographic region and one or moreother geographic regions; determining a second score using the KPI data;generating a network score for the first geographic region based on thefirst score and the second score; identifying a network equipment toreconfigure using the generated network score; determining an adjustmentto an operational parameter of the network equipment using the generatednetwork score; and transmitting an instruction to the network equipment,wherein the instruction comprises an indication of the determinedadjustment to the operational parameter, wherein reception of theinstruction causes the network equipment to adjust the operationalparameter according to the determined adjustment.
 2. Thecomputer-implemented method of claim 1, wherein identifying a networkequipment to reconfigure further comprises applying the network score asan input to a machine learning model, wherein execution of the machinelearning model results in an identification of the network equipment andthe adjustment to the operational parameter.
 3. The computer-implementedmethod of claim 2, wherein the machine learning model is trained basedon training data that comprises at least one of information identifyinga past network score of a second geographic region, a past KPI value ofthe second geographic region, an identification of a second networkequipment that was reconfigured in the second geographic region, asecond operational parameter that was adjusted, an indication of achange in a second network score of the second geographic region inresponse to adjustment of the second operational parameter, networkscore trend data, or KPI trend data.
 4. The computer-implemented methodof claim 1, further comprising generating user interface data, whereinthe user interface data, when executed by a user device, causes the userdevice to display an interactive user interface depicting the networkscore.
 5. The computer-implemented method of claim 4, wherein theinteractive user interface includes a shape corresponding to the firstgeographic region, wherein selection of the shape causes the interactiveuser interface to display a portion of the KPI data corresponding to thefirst geographic region.
 6. The computer-implemented method of claim 1,wherein generating the network score further comprises: comparing theKPI goal data with a portion of the KPI data corresponding to the firstgeographic region to determine the first score; comparing the portion ofthe KPI data corresponding to the first geographic region with anotherportion of the KPI data corresponding to the one or more othergeographic regions to determine the second score; and aggregating thefirst score and the second score.
 7. The computer-implemented method ofclaim 6, wherein aggregating the first score and the second scorefurther comprises: retrieving extra metrics data for the firstgeographic region; processing the extra metrics data to determine anextra score; and aggregating the first score, the second score, and theextra score.
 8. The computer-implemented method of claim 1, wherein thenetwork equipment is located within an access network of atelecommunications network.
 9. The computer-implemented method of claim8, wherein the network equipment comprises one of a cell site, a basestation, or a cellular tower.
 10. The computer-implemented method ofclaim 1, wherein the KPI data comprises at least one of voiceaccessibility data, voice drop call rate data, session initiationprotocol (SIP) drop call rate data, combined drop call rate data, userequipment (UE) downlink throughput data, UE uplink throughput data,leakage data, network interference data, signal strength data, voicetraffic data, or data traffic data.
 11. Non-transitory,computer-readable storage media comprising computer-executableinstructions, wherein the computer-executable instructions, whenexecuted by a computer system, cause the computer system to: retrievekey performance indicator (KPI) goal data for a first geographic region;determine a first score using the KPI goal data; retrieve KPI data forthe first geographic region and a second geographic region; determine asecond score using the KPI data; generate a network score for the firstgeographic region based on the first score and the second score;determine an adjustment to an operational parameter of a networkequipment using the generated network score; and transmit an instructionto the network equipment, wherein the instruction comprises anindication of the determined adjustment to the operational parameter,wherein reception of the instruction causes the network equipment toadjust the operational parameter according to the determined adjustment.12. The non-transitory, computer-readable storage media of claim 11,wherein the computer-executable instructions further cause the computersystem to apply the network score as an input to a machine learningmodel, wherein execution of the machine learning model results in anidentification of the network equipment and the adjustment to theoperational parameter.
 13. The non-transitory, computer-readable storagemedia of claim 12, wherein the machine learning model is trained basedon training data that comprises at least one of information identifyinga past network score of a second geographic region, a past KPI value ofthe second geographic region, an identification of a second networkequipment that was reconfigured in the second geographic region, asecond operational parameter that was adjusted, an indication of achange in a second network score of the second geographic region inresponse to adjustment of the second operational parameter, networkscore trend data, or KPI trend data.
 14. The non-transitory,computer-readable storage media of claim 11, wherein thecomputer-executable instructions further cause the computer system togenerate user interface data, wherein the user interface data, whenexecuted by a user device, causes the user device to display aninteractive user interface depicting the network score.
 15. Thenon-transitory, computer-readable storage media of claim 14, wherein theinteractive user interface includes a shape corresponding to the firstgeographic region, wherein selection of the shape causes the interactiveuser interface to display a portion of the KPI data corresponding to thefirst geographic region.
 16. A system comprising: a data storeconfigured to store key performance indicator (KPI) data for a firstgeographic region and a second geographic region and KPI goal data forthe first geographic region; and a network equipment operationadjustment system comprising a processor in communication with the datastore and configured with specific computer-executable instructions to:determine a first score using the KPI goal data; retrieve the KPI data;determine a second score using the KPI data; generate a network scorefor the first geographic region based on the first score and the secondscore; determine an adjustment to an operational parameter of a networkequipment using the generated network score; and transmit an instructionto the network equipment, wherein the instruction comprises anindication of the determined adjustment to the operational parameter,wherein reception of the instruction causes the network equipment toadjust the operational parameter according to the determined adjustment.17. The system of claim 16, wherein the network equipment operationadjustment system is further configured with specificcomputer-executable instructions to apply the network score as an inputto a machine learning model, wherein execution of the machine learningmodel results in an identification of the network equipment and theadjustment to the operational parameter.
 18. The system of claim 17,wherein the machine learning model is trained based on training datathat comprises at least one of information identifying a past networkscore of a second geographic region, a past KPI value of the secondgeographic region, an identification of a second network equipment thatwas reconfigured in the second geographic region, a second operationalparameter that was adjusted, an indication of a change in a secondnetwork score of the second geographic region in response to adjustmentof the second operational parameter, network score trend data, or KPItrend data.
 19. The system of claim 16, wherein the network equipmentoperation adjustment system is further configured with specificcomputer-executable instructions to generate user interface data,wherein the user interface data, when executed by a user device, causesthe user device to display an interactive user interface depicting thenetwork score.
 20. The system of claim 19, wherein the interactive userinterface includes a shape corresponding to the first geographic region,wherein selection of the shape causes the interactive user interface todisplay a portion of the KPI data corresponding to the first geographicregion.
 21. A computer-implemented method comprising: retrieving keyperformance indicator (KPI) goal data for a first geographic region;retrieving KPI data for the first geographic region and one or moreother geographic regions; comparing the KPI goal data with a portion ofthe KPI data corresponding to the first geographic region to determine aregion score; comparing the portion of the KPI data corresponding to thefirst geographic region with another portion of the KPI datacorresponding to the one or more other geographic regions to determine anational score; aggregating the region score and the national score togenerate a network score for the first geographic region; identifying anetwork equipment to reconfigure using the generated network score;determining an adjustment to an operational parameter of the networkequipment using the generated network score; and transmitting aninstruction to the network equipment, wherein the instruction comprisesan indication of the determined adjustment to the operational parameter,wherein reception of the instruction causes the network equipment toadjust the operational parameter according to the determined adjustment.22. The computer-implemented method of claim 21, wherein aggregating theregion score and the national score further comprises: retrieving extrametrics data for the first geographic region; processing the extrametrics data to determine an extra score; and aggregating the regionscore, the national score, and the extra score.